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Cancer is a disease that occurs due to the uncontrolled growth of abnormal cells causing body tissue damage. This disease is considered as a deadly disease. In 2019, 1700 deaths occur every day due to cancer. Some effective anti-cancer agents are known to cause temporary to chronic toxic effects. There are several compounds that have the potential to become anticancer drugs, one of them is indenopyrazole. Recently, not many QSAR studies have been conducted to exploit the potential of indenopyrazole as anti-cancer agent. The goal of this research is to implement simulated annealing and support vector machine method in the QSAR study to predict the activity of indenopyrazole derivatives as anti-cancer drugs. Simulated annealing is used for feature selection and support vector machine is used for model development. In this research, we used three kernel models for SVM, namely SVM with RBF kernel, SVM with linear kernel, and SVM with the polynomial kernel. From three models that were regressed, SVM with RBF kernel has parameter C=10, gamma=scale and epsilon=0.1 produce R2 score train and test 0.79 and 0.60, respectively. SVM with linear kernel has parameter C=1000, degree=1 and epsilon=0.1 produce R2 score train and test 0.61 and 0.63, respectively. SVM with polynomial kernel has parameter C=1000, degree=2 and epsilon=0.1 produce R2 score train and test 0.72 and 0.50, respectively. Based on the validation results, only model with RBF kernel which parameters satisfy all the criteria. From the result we can conclude that the model with RBF kernel is the best model and acceptable.

Malaria is a disease caused by the Plasmodium falciparum parasite and leads to many cases of deaths. Recently, the combination of several drugs has been used to treat this disease. However, the parasite is known to be resistant to the anti-malarial agent. Hence, a new candidate for an anti-malarial drug is required to solve the resistance problem. One compound that is promising as an anti-malarial agent is fusidic acid derivatives. Fusidic acid is an antibiotic that is work by preventing parasite growth. Besides, fusidic acid is known to have antiplasmodial activity although the IC50 is still poor. However, the activity can be improved by optimizing the structure through its derivatives. In this study, we developed a QSAR model to predict the activity of fusidic acid derivatives as anti-malarial agent. The model was developed by using Simulated Annealing (SA) for feature selection and Support Vector Machine (SVM) for model development. The results show that SA produces a satisfying combination of features that are indicated by the trend of MSE value during the selection process. Regarding the performance, SVM with RBF kernel produces the best result of the validation parameter. This indicates that the model is valid to be used to predict a compound with unknown activity values for anti-malarial agents.

In the last decade, Bandung has become one of the tourism destination places in Indonesia. It is reported that almost 6.7 million visitors come to Bandung in 2018, and the number increased by almost 4% per year since 2014. This rise in the number of visitors leads to the establishment of several toll gates as main access points throughout the city. As one of the busiest ones, Buah Batu toll gate is frequently congested because of the location that is close to the southern part of Bandung. To overcome this problem, a traffic regulation based on computer simulation is urgently required. In this study, we simulate the traffic system on the Buah Batu toll gate by using a combination of Nagel-Schreckenberg (NaSch) and Daoudia and Moussa (DM) models. NaSch model was used to defined vehicle movement, while the DM model was used to allow a vehicle to change lane. We defined three scenarios to evaluate the effectivity of the closing gate scheme. We found that the closing of gate 5 is more effective than the closing of gate 1. We also investigated the contribution of traffic density and driver’s behavior, e.g., stopping behavior and lane-changing behavior, to the average velocity of the vehicles.

Selecting the best talents from a large number of job applicants is challenging, especially for big companies that usually receive tens of thousands of applicants for every job opening. One of the most costly and time-consuming applicant selection stages is the interview process, since it usually performs face to face meetings and involves third parties to do the interviews and analyze the result. To this end, Human Capital Directorate at Telkom Indonesia adopts AI technology to automate some stages of job applicant selection to reduce manual process and third-party involvement. In this paper, we investigate appropriate feature extraction methods to automate job interview grading for reducing bias and human errors. TFIDF, one of the most popular feature extractions, is compared with word embedding to find the optimal method and parameters in classifying interview verbatims with ANN classifier. Based on the test results, the average accuracy for TFIDF outperforms word embedding by 85.22% against 74.88%, respectively. Therefore, for the case of job interview grading using our dataset, TF-IDF performs better to reduce the number of dimensions.

Malaria is a disease that caused many adverse effects on humans. Various attempts have been done to find new anti-malarial agents due to the resistance problem of the existing drug. Fusidic acid is known as one of a compound that is promising to be used as an anti-malaria agent. However, this compound should be derived to obtain a new fusidic acid derivative that has better activity. The exploration of the compound in conventional style has a shortcoming in the term of time and cost. Therefore, an alternative method is required to accelerate the design. In this study, we applied a quantitative structure-activity relationship (QSAR) to produce a predictive model. The produced model can be used to predict the activity of the compound as an anti-malaria agent. The development of the model was performed by using genetic algorithm (GA) for feature selection and artificial neural network (ANN) for model development. We developed five models by utilizing a different number of the descriptor in each model. The validation process was performed by evaluating several validation parameters, such as accuracy. According to the results, we found that the model 3, which is comprised of seven descriptors, produce a better result with the accuracies of internal and external data set are 0.96 and 0.92, respectively.